# Where Do Human Heuristics Come From?

## TL;DR

This paper explores the origins of human heuristics by modeling them as learned, resource-bounded approximations to optimal decision-making, supported by empirical evidence from a bandit task.

## Contribution

It introduces a novel meta-learning framework combining RNNs with resource constraints to explain human heuristic strategies.

## Key findings

- Models exhibit patterns similar to human decision-making differences.
- Supports the hypothesis that heuristics are learned, resource-bounded approximations.
- Connects heuristics to variational inference and MDL principles.

## Abstract

Human decision-making deviates from the optimal solution, that maximizes cumulative rewards, in many situations. Here we approach this discrepancy from the perspective of bounded rationality and our goal is to provide a justification for such seemingly sub-optimal strategies. More specifically we investigate the hypothesis, that humans do not know optimal decision-making algorithms in advance, but instead employ a learned, resource-bounded approximation. The idea is formalized through combining a recently proposed meta-learning model based on Recurrent Neural Networks with a resource-bounded objective. The resulting approach is closely connected to variational inference and the Minimum Description Length principle. Empirical evidence is obtained from a two-armed bandit task. Here we observe patterns in our family of models that resemble differences between individual human participants.